Abstract
Extended state observer (ESO) has been widely used to deal with various control and filtering problems of uncertain systems. In the frame of ESO, the “total disturbance”in systems can betimely estimated from the input-data and output-data of systems. This talk will discuss the data-driven mechanism of ESO to extract the hidden information of uncertainties. The existing design methods and theoretical analysis of ESO based control will be reviewed. Next, I will focus on the optimization problem of estimating both states and uncertain dynamics, especially for the stochastic systems. The available methods and theoryofextended state based Kalman filter and distributed Kalman filters will bedemonstrated.
